Fast Time-Varying Sparse Channel Estimation Based on Kalman Filter

YUAN Weina; WANG Jiaxuan

doi:10.3969/j.issn.0258-2724.2018.04.023

Volume 31 Issue 4

Jul. 2018

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Journal of Southwest Jiaotong University > 2018 > 53(4): 835-841.

YUAN Weina, WANG Jiaxuan. Fast Time-Varying Sparse Channel Estimation Based on Kalman Filter[J]. Journal of Southwest Jiaotong University, 2018, 53(4): 835-841. doi: 10.3969/j.issn.0258-2724.2018.04.023

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YUAN Weina, WANG Jiaxuan. Fast Time-Varying Sparse Channel Estimation Based on Kalman Filter[J]. Journal of Southwest Jiaotong University, 2018, 53(4): 835-841. doi: 10.3969/j.issn.0258-2724.2018.04.023

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Fast Time-Varying Sparse Channel Estimation Based on Kalman Filter

doi: 10.3969/j.issn.0258-2724.2018.04.023

Received Date: 11 Dec 2017
Publish Date: 01 Aug 2018

Abstract

Abstract

A fast time-varying sparse channel estimation method based on the Kalman filter is proposed for channel estimation of an orthogonal frequency division multiplexing communication system operating in high-speed railways and mountain areas. Based on the basic expansion model (BEM), compressed sensing (CS) was employed for the estimation of sparse delays, and a Kalman filter (KF) estimator was utilised for estimating the BEM coefficients. The channel gains were then computed easily. The simulation results show that under the same signal-to-ratio (SNR) condition, with the increase in frequency-normalised Doppler shift (FND), the MSE of the new method is superior to that of traditional methods, such as SNR is 20 dB and FND is 0.1, and a 4 dB performance improvement is achieved. Under the same Doppler shift condition, the same result is obtained as that with the increase in SNR, such as FND is 0.2 and MSE is 0.06, and a 6 dB SNR gain is achieved. These results show that the new method is more robust to variation in channel time and stronger against noise compared with traditional methods.
- channel estimation,
- basic expansion model,
- Kalman filter,
- compressed sensing

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References(17)

References

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Fast Time-Varying Sparse Channel Estimation Based on Kalman Filter

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